Equal-time kinetic equations in a rotational field

We investigate quantum kinetic theory for a massive fermion system under a rotational field. From the Dirac equation in curved space we derive the complete set of kinetic equations for the spin components of the covariant and equal-time Wigner functions. While the particles are no longer on a mass shell in general case due to the rotation-spin coupling, there are always only two independent components, which can be taken as the number and spin densities. With the help from the off-shell constraint we obtain the closed transport equations for the two independent components in classical limit and at quantum level. The classical rotation-orbital coupling controls the dynamical evolution of the number density, but the quantum rotation-spin coupling explicitly changes the spin density.Comment: 12 page

Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions

Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language tasks such as open-domain knowledge-based visual question answering (OK-VQA). As images are invisible to LLMs, researchers convert images to text to engage LLMs into the visual question reasoning procedure. This leads to discrepancies between images and their textual representations presented to LLMs, which consequently impedes final reasoning performance. To fill the information gap and better leverage the reasoning capability, we design a framework that enables LLMs to proactively ask relevant questions to unveil more details in the image, along with filters for refining the generated information. We validate our idea on OK-VQA and A-OKVQA. Our method continuously boosts the performance of baselines methods by an average gain of 2.15% on OK-VQA, and achieves consistent improvements across different LLMs.Comment: Accepted to EMNLP2023 Finding

The prophages of Citrobacter rodentium represent a conserved family of horizontally-acquired mobile genetic elements associated with enteric evolution towards pathogenicity

Prophage mediated horizontal gene transfer (HGT) plays a key role in the evolution of bacteria, enabling access to new environmental niches, including pathogenicity. Citrobacter rodentium is a host-adapted intestinal mouse pathogen and important model organism for attaching and effacing (A/E) pathogens including the clinically significant enterohaemorrhagic (EHEC) and enteropathogenic (EPEC) Escherichia coli. Despite containing ten prophage genomic regions, including an active temperate phage, ΦNP, little was known regarding the nature of C. rodentium prophages in the bacterium’s evolution towards pathogenicity. In this study, our characterization of ΦNP led to the discovery of a second, fully functional temperate phage, named ΦSM. We identify the bacterial host-receptor for both phages as lipopolysaccharide (LPS). ΦNP and ΦSM are likely important mediators of HGT in C. rodentium. Bioinformatic analysis of the ten prophage regions reveals cargo genes encoding known virulence factors, including several Type III secretion system (T3SS) effectors. C. rodentium prophages are conserved across a wide range of pathogenic enteric bacteria, including EPEC and EHEC as well as pathogenic strains of Salmonella enterica, Shigella boydii, and Klebsiella pneumoniae. Phylogenetic analysis of core enteric backbone genes compared against prophage evolutionary models suggests that these prophages represent an important, conserved family of horizontally acquired enteric-associated pathogenicity determinants. In addition to highlighting the transformative role of bacteriophage mediated HGT in C. rodentium’s evolution towards pathogenicity, these data suggest that the examination of conserved families of prophages in other pathogenic bacteria and disease outbreaks might provide deeper evolutionary and pathological insights otherwise obscured by more classical analysis.BBSRC and China Scholarship Council and the Cambridge Commonwealth, European, and International Trust

Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, the low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular application that motivates this study is the spatiotemporal data analysis. As shown in the preliminary study, weakly dependencies can worsen the low-rank tensor completion performance. In this paper, we propose a novel low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework by introducing the $L_{1}$-norm penalty and Graph Laplacian penalty to model the weakly dependency on graph. We further propose an efficient optimization algorithm based on the Block Coordinate Descent for efficient estimation. A case study based on the metro passenger flow data in Hong Kong is conducted to demonstrate improved performance over the regular tensor completion methods.Comment: Accepted at AAAI 202

Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding

We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for combining diverse visual / textual features and Bidirectional Negation Learning (BNL) for addressing queries that contain negation cues. In particular, LAFF performs feature fusion at both early and late stages and at both text and video ends to exploit diverse (off-the-shelf) features. Compared to multi-head self attention, LAFF is much more compact yet more effective. Its attentional weights can also be used for selecting fewer features, with the retrieval performance mostly preserved. BNL trains a negation-aware video retrieval model by minimizing a bidirectionally constrained loss per triplet, where a triplet consists of a given training video, its original description and a partially negated description. For video feature extraction, we use pre-trained CLIP, BLIP, BEiT, ResNeXt-101 and irCSN. As for text features, we adopt bag-of-words, word2vec, CLIP and BLIP. Our training data consists of MSR-VTT, TGIF and VATEX that were used in our previous participation. In addition, we automatically caption the V3C1 collection for pre-training. The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team. Our best run, with an infAP of 0.262, is ranked at the second place teamwise

Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering

Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip information, including multiple trips within each passenger and multi-dimensional information about each trip. Furthermore, existing approaches rely on an accurate specification of the clustering number to start. Finally, existing methods do not consider spatial semantic graphs such as geographical proximity and functional similarity between the locations. In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically. The spatial graphs are utilized in community detection to link the semantic neighbors. We further propose a tensor version of Collapsed Gibbs Sampling method with a minimum cluster size requirement. A case study based on Hong Kong metro passenger data is conducted to demonstrate the automatic process of cluster amount evolution and better cluster quality measured by within-cluster compactness and cross-cluster separateness. The code is available at https://github.com/bonaldli/TensorDPMM-G.Comment: Accepted in ACM SIGSPATIAL 2023. arXiv admin note: substantial text overlap with arXiv:2306.1379

Distribution and Determinants of Correlation between PM2.5 and O3 in China Mainland: Dynamitic simil-Hu Lines

In recent years, China has made great efforts to control air pollution. During the governance process, it is found that fine particulate matter (PM2.5) and ozone (O3) change in the same trend among some areas and the opposite in others, which brings some difficulties to take measures in a planned way. Therefore, this study adopted multi-year and large-scale air quality data to explore the distribution of correlation between PM2.5 and O3, and proposed a concept called dynamic similar hu lines to replace the single fixed division in the previous research. Furthermore, this study discussed the causes of distribution patterns quantitatively with geographical detector and random forest. The causes included natural factors and anthropogenic factors. And these factors could be divided into three parts according to the characteristics of spatial distribution: broadly changing with longitude, changing with latitude, and having local characteristics. Overall, regions with relatively more densely population, higher GDP, lower altitude, higher humidity, higher atmospheric pressure, higher surface temperature, less sunshine hours and more accumulated precipitation often corresponds to positive correlation coefficient between PM2.5 and O3, no matter in which season. The parts with opposite conditions that mentioned above are essentially negative correlation coefficient. And what's more, humidity, global surface temperature, air temperature and accumulated precipitation are four decisive factors to form the distribution of correlation between PM2.5 and O3. In general, collaborative governance of atmospheric pollutants should consider particular time and space background and also be based on the local actual socio-economic situations, geography and geomorphology, climate and meteorology and other comprehensive factors.Comment: Our research group have decided to withdraw this preprin